A hybrid neuro--wavelet predictor for QoS control and stability

For distributed systems to properly react to peaks of requests, their adaptation activities would benefit from the estimation of the amount of requests. This paper proposes a solution to produce a short-term forecast based on data characterising user behaviour of online services. We use \emph{wavelet analysis}, providing compression and denoising on the observed time series of the amount of past user requests; and a \emph{recurrent neural network} trained with observed data and designed so as to provide well-timed estimations of future requests. The said ensemble has the ability to predict the amount of future user requests with a root mean squared error below 0.06\%. Thanks to prediction, advance resource provision can be performed for the duration of a request peak and for just the right amount of resources, hence avoiding over-provisioning and associated costs. Moreover, reliable provision lets users enjoy a level of availability of services unaffected by load variations

The Effects of Direct Instruction Flashcards and Rewards with Math Facts at School and in the Home: Acquisition and Maintenance

The purpose of the present study was to evaluate the effects of Direct Instruction (DI) flashcard procedure, combined with strategies and rewards on multiplication fact accuracy of two elementary school-age students. A single subject replication design across three and four sets of multiplication facts was used to evaluate outcomes. The results indicated improvement in math performance for each participant. Follow-up data indicated maintenance of treatment effects over time. Finally, pre and posttest outcomes found generalization to correct writing of math facts for each participant. The benefits of employing DI flashcards in a resource room or home were discussed

The Dynamic Insulin Sensitivity and Secretion Test (DISST) - a novel measure of insulin sensitivity

Objective: To validate the methodology for the Dynamic Insulin Sensitivity and Secretion Test (DISST) and to demonstrate its potential in clinical and research settings. Methods: 123 men and women had routine clinical and biochemical measurements, an oral glucose tolerance test and a DISST. For the DISST, participants were cannulated for blood sampling and bolus administration. Blood samples were drawn at t=0, 10, 15, 25 and 35 minutes for measurement of glucose, insulin and C-peptide. A 10g bolus of intravenous glucose at t=5 minutes and 1U of intravenous insulin immediately after the t=15 minute sample were given. Fifty participants also had a hyperinsulinaemic euglycaemic clamp. Relationships between DISST insulin sensitivity (SI) and the clamp, and both DISST SI and secretion and other metabolic variables were measured. Results: A Bland-Altman plot showed little bias in the comparison of DISST with the clamp; with DISST underestimating the glucose clamp by 0.1·10-2·mg·l·kg-1·min-1·pmol-1 (90%CI -0.2 to 0). The correlation between SI as measured by DISST and the clamp was 0.82, the c unit for the ROC analysis for the two tests was 0.96. Metabolic variables showed significant correlations with DISST IS, and the second phase of insulin release. DISST also appears able to distinguish different insulin secretion patterns in individuals with identical SI values. Conclusions: DISST is a simple, dynamic test that compares favourably with the clamp in assessing SI and allows simultaneous assessment of insulin secretion. DISST has the potential to provide even more information about the pathophysiology of diabetes than more complicated tests

The Health Belief Model Applied to Understanding Diabetes Regimen Compliance

Inadequate adherence to prescribed treatment plans is perhaps the most serious obstacle to achieving success ful therapeutic outcomes, and non compliance by diabetic patients is no exception. This is partly based on pa tients' realization that compliance does not necessarily result in lack of illness. A psychosocial framework for under standing patient compliance is the Health Belief Model, which is based upon the value an individual places on the identified goal and the likelihood that compliance will achieve that goal. This Model has been useful to explain noncompliance, to make an "educa tional diagnosis," and for designing compliance-enhancing interventions.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68410/2/10.1177_014572178501100108.pd

Data mining with neural networks and support vector machines using the R/rminer tool

We present rminer, our open source library for the R tool that facilitates the use of data mining (DM) algorithms, such as neural Networks (NNs) and support vector machines (SVMs), in classification and regression tasks. Tutorial examples with real-world problems (i.e. satellite image analysis and prediction of car prices) were used to demonstrate the rminer capabilities and NN/SVM advantages. Additional experiments were also held to test the rminer predictive capabilities, revealing competitive performances.Fundação para a Ciência e a Tecnologia (FCT) - PTDC/EIA/64541/200

Socially impaired robots: Human social disorders and robots’ socio-emotional intelligence

© Springer International Publishing Switzerland 2014. Social robots need intelligence in order to safely coexist and interact with humans. Robots without functional abilities in understanding others and unable to empathise might be a societal risk and they may lead to a society of socially impaired robots. In this work we provide a survey of three relevant human social disorders, namely autism, psychopathy and schizophrenia, as a means to gain a better understanding of social robots’ future capability requirements.We provide evidence supporting the idea that social robots will require a combination of emotional intelligence and social intelligence, namely socio-emotional intelligence. We argue that a robot with a simple socio-emotional process requires a simulation-driven model of intelligence. Finally, we provide some critical guidelines for designing future socio-emotional robots

Anthropogenic Space Weather

Anthropogenic effects on the space environment started in the late 19th century and reached their peak in the 1960s when high-altitude nuclear explosions were carried out by the USA and the Soviet Union. These explosions created artificial radiation belts near Earth that resulted in major damages to several satellites. Another, unexpected impact of the high-altitude nuclear tests was the electromagnetic pulse (EMP) that can have devastating effects over a large geographic area (as large as the continental United States). Other anthropogenic impacts on the space environment include chemical release ex- periments, high-frequency wave heating of the ionosphere and the interaction of VLF waves with the radiation belts. This paper reviews the fundamental physical process behind these phenomena and discusses the observations of their impacts.Comment: 71 pages, 35 figure